Cognitive and motor profiles as prodromal markers in predicting phenoconversion and phenotype in isolated REM sleep behavior disorder.
Bibliographic record
Abstract
OBJECTIVE: To determine the clinical markers based on cognitive and motor profiles in predicting phenoconverion and phenotype in isolated rapid eye movement sleep behavior disorder (iRBD). METHODS: 45 iRBD patients and 25 healthy controls were included in the follow-up study. All participates received comprehensive evaluations of cognitive, motor and autonomic function at baseline. Positive phenoconversion were identified according to standard diagnostic criteria during follow-up. RESULTS: 21 iRBD patients displayed phenoconversion in a mean follow-up of 2.9 ± 1.6 years, with 14 presenting motor phenotype and 7 cognitive phenotype. In iRBD, visuospatial, memory, attention-executive function, information processing speed, and motor function predicted phenoconversion, with the combination of Trail Making Test (TMT) and Alternate-tap Test (ATT) performing best (sensitivity = 95.0 %, specificity = 75.0 %); attention-executive function, information processing speed, and motor function predicted motor phenotype conversion, with the combination of TMT and ATT performing best (sensitivity = 100 %, specificity = 66.7 %); visuospatial, memory, and attention-executive function predicted cognitive phenotype conversion, with TMT performing best (sensitivity = 83.3 %, specificity = 91.7 %). Furthermore, individuals with lower z-scores of TMT, Symbol Digit Modalities Test, and ATT than the established cutoff values in iRBD exhibited a significantly higher risk for phenoconversion at follow-up (HR = 2.98, 9.53, 11.68; respectively). CONCLUSIONS: In iRBD, the attention-executive and motor function served as optimum combined markers in predicting phenoconversion and motor phenotype, whereas the attention-executive function performed best in predicting cognitive phenotype. Poor attention-executive function, information processing speed and motor function in iRBD independently increased the risk of phenoconversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".